Category-based selections in an information access environment

• US 20030018659A1
• Filed: 03/13/2002
• Published: 01/23/2003
• Est. Priority Date: 03/14/2001
• Status: Abandoned Application

• Current Assignee: LingoMotors, Inc.
• Original Assignee: LingoMotors, Inc.
• Inventors: Pavlovitz, Ofra, Fuks, Avi, Dagan, Ido, Yellin, Ido
• Application Number:
  US10/099,904
• Publication Number:
  US 20030018659A1
• Time in Patent Office: Days
• US Class Current: 715/500
• CPC Class Codes: G06F 16/3325 Reformulation based on results of preceding query
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method for scoring indexing concepts for their relevancy in the context, including obtaining a collection of documents, classifying the collection of documents to a set of indexing concepts and scoring each indexing concept according to the relevancy of the indexing concept to the collection of documents.

Claims

1. A method for scoring indexing concepts for their relevancy in the context, comprising:

(One) obtaining a collection of documents;

(Two) classifying the collection of documents to a set of indexing concepts;

(Three) scoring each indexing concept according to at least the relevancy of the indexing concept to said collection of documents.

2. The method according to claim 1, wherein said indexing concepts being categories arranged in a hierarchy.

3. The method according to claim 1, wherein said collection of documents is obtained as a result of a query.

4. The method according to claim 2, wherein said collection of documents is obtained as a result of a query.

5. The method according to claim 1, further comprising the step of displaying or not each one of said indexing concept depending upon at least its respective indexing concept score.

6. The method according to claim 2, further comprising the step of displaying or not each one of said indexing concept depending upon at least its respective indexing concept score.

7. The method according to claim 1, wherein:

said step (a) includes obtaining Document relevancy scores in the context;

said step (b) includes obtaining Document-category classification scores; and

said step (c) includes calculating Category relevancy scores in the context as a function of at least said document relevancy scores in the context and said document-category classification scores.

8. The method according to claim 2, wherein:

said step (a) includes obtaining Document relevancy scores in the context;

said step (b) includes obtaining Document-category classification scores; and

said step (c) includes calculating Category relevancy scores in the context as a function of at least said document relevancy scores in the context and said document-category classification scores.

9. The method according to claim 7, wherein said step (c), further includes taking into account at least one non-context related factor.

10. The method according to claim 8, wherein said step (c), further includes taking into account at least one non-context related factor.

11. The method according to claim 7, wherein said Document-category classification scores are determined a priori.

12. The method according to claim 7, wherein said Document-category classification scores are determined in a dynamic fashion.

13. The method according to claim 8, wherein said Document-category classification scores are determined a priori.

14. The method according to claim 8, wherein said Document-category classification scores are determined in a dynamic fashion.

15. The method according to claim 7, wherein said function includes a scalar product.

16. The method according to claim 8, wherein said function includes a scalar product.

17. The method according to claim 15, wherein said function further takes into account relative size of group of documents within category.

18. The method according to claim 16, wherein said function further takes into account relative size of group of documents within category.

20. The method according to claim 19, wherein said indexing concepts being categories arranged in a hierarchy.

21. The method according to claim 20, wherein said collection of documents is obtained as a result of a query.

22. The method according to claim 21, wherein said collection of documents is obtained as a result of a query.

23. The method according to claim 19, further comprising the step of displaying or not each one of the propositions depending upon at least its respective propositions score.

24. The method according to claim 20, further comprising the step of displaying or not each one of the propositions depending upon at least its respective propositions score.

25. The method according to claim 19, wherein at least one of said propositions being a business-related proposition.

26. The method according to claim 20, wherein at least one of said propositions being a business-related proposition.

27. The method according to claim 19, wherein at least one of said propositions being a non business-related proposition.

28. The method according to claim 20, wherein at least one of said propositions being a non business-related proposition.

29. The method according to claim 19, wherein:

said step (a) includes obtaining Document relevancy scores in the context;

said step (b) includes obtaining Document-category classification scores; and

said step (c) includes calculating Category relevancy scores in the context as a function of at least said document relevancy scores in the context and said document-category classification scores; and

said step (d) includes;

obtaining Proposition-category relevancy scores;

calculating Proposition relevancy scores in the context as a function of at least said category relevancy scores in the context and proposition-category relevancy scores.

30. The method according to claim 20, wherein:

said step (a) includes obtaining Document relevancy scores in the context;

said step (b) includes obtaining Document-category classification scores; and

said step (c) includes calculating Category relevancy scores in the context as a function of at least said document relevancy scores in the context and said document-category classification scores; and

said step (d) includes;

obtaining Proposition-category relevancy scores;

calculating Proposition relevancy scores in the context as a function of at least said category relevancy scores in the context and proposition-category relevancy scores.

31. The method according to claim 29, wherein said step (d) further includes

obtaining proposition significance scores;

calculating Proposition relevancy scores in the context as a function of at least said category relevancy scores in the context, and proposition-category relevancy scores, and further take into account a non context factor including said proposition significance scores.

32. The method according to claim 30, wherein said step (d) further includes

obtaining proposition significance scores;

calculating Proposition relevancy scores in the context as a function of at least said category relevancy scores in the context, and proposition-category relevancy scores, and further take into account a non context factor including said proposition significance scores.

33. The method according to claim 29, wherein said collection of documents being collection of TV programs and wherein said categories being TV program categories, and further comprising the step of promoting at least one proposition according to the respective proposition relevance score in the context.

34. The method according to claim 30, wherein said collection of documents being collection of TV programs and wherein said categories being TV program categories, and further comprising the step of promoting at least one proposition according to the respective proposition relevance score in the context.

35. The method according to claim 29, wherein said collection of documents being collection of cookie files and wherein said categories being a preference category of a group of people, and further comprising the step of promoting at least one proposition according to the respective proposition relevance score in the context.

36. The method according to claim 30, wherein said collection of documents being collection of cookie files and wherein said categories being a preference category of a group of people, and further comprising the step of promoting at least one proposition according to the respective proposition relevance score in the context.

39. The system according to claim 38, wherein said system is configured to:

obtain Document relevancy scores in the context;

obtain Document-category classification scores; and

calculate Category relevancy scores in the context as a function of at least said document relevancy scores in the context and said document-category classification scores.

42. A computer program product that includes a computer program code configured to perform the method steps of claim 1.

43. A computer program product that includes a computer program code configured to perform the method steps of claim 19.

19. A method for scoring propositions for their relevancy in the context, comprising:

(One) obtaining a collection of documents;

(Two) classifying the collection of documents to a set of indexing concepts;

(Three) scoring each indexing concept according to at least the relevancy of the indexing concept to said collection of documents;

(Four)scoring each proposition according to at least the relevancy of the proposition to the collection of the documents.

37. A method for real time targeting of advertisements to viewers, comprising pushing distinct advertisements to distinct viewers substantially simultaneously according to the relevance of the distinct advertisements to the distinct viewers.

44. A computer program product that includes a computer program code configured to perform the method steps of claim 37.

38. A system including a computer and associated memory for scoring indexing concepts for their relevancy in the context, the system is configured to perform the following, including:

One) obtaining a collection of documents;

Two) classifying the collection of documents to a set of indexing concepts; and

Three) scoring each indexing concept according to at least the relevancy of the indexing concept to said collection of documents.

40. A system including a computer and associated memory for scoring indexing concepts for their relevancy in the context, the system is configured to perform the following, including:

One) obtaining a collection of documents;

Two) classifying the collection of documents to a set of indexing concepts;

Three) scoring each indexing concept according to at least the relevancy of the indexing concept to said collection of documents;

Four) scoring each proposition according to at least the relevancy of the proposition to the collection of the documents.

41. The system according to claim 40, wherein said system is configured to:

obtain Document relevancy scores in the context;

obtain Document-category classification scores; and

calculate Category relevancy scores in the context as a function of at least said document relevancy scores in the context and said document-category classification scores;

obtain Proposition-category relevancy scores; and

calculate Proposition relevancy scores in the context as a function of at least said category relevancy scores in the context and proposition-category relevancy scores.